Embedding and clustering.
Recently, machine learning techniques such as semantic embedding [1]} have been investigated for ontology alignment.
They often first learn vector representations of the entities and then predict the alignment [2]}, [3]}, [4]}.
However, most of them focus on alignment of ontology individuals (i.e., ABox) without considering the ontology concepts and axioms at the terminological level (i.e., TBox).
Nkisi-Orji et al. [5]} predicts the alignment between ontology concepts with Random Forest, but incorporates the embeddings of words alone, without any other semantic components like in our work.
Furthermore, these approaches focus on predicting the alignment,
while our work aims at boosting an existing alignment system. Our framework could potentially be adopted in systems like in [5]} if facing scalability problems for large ontologies.
